A Bayesian Information System for Predicting Stator Faults in Induction Machines

Open access


The approach adopted in this paper focuses on the faults prediction in asynchronous machines. The main goal is to explore interesting information regarding the diagnosis and prediction of electrical machines failures by the use of a Bayesian graphical model. The Bayesian forecasting model developed in this paper provides a posteriori probability for faults in each hierarchical level related to the breakdowns process. It has the advantage that it can give needed information’s for maintenance planning. A real industrial case study is presented in which the maintenance staff expertise has been used to identify the structure of the Bayesian network and completed by the parameters definition of the Bayesian network using historical file data of an induction motor. The robustness of the proposed methodology has also been tested. The results showed that the Bayesian network can be used for safety, reliability and planning applications.

[1] Gill, P. “Electrical power equipment maintenance and testing”, 2nd ed. Taylor & Francis Group, LLC., 2009.

[2] Moghadasian, M., Shakouhi, S. M., and Moosavi, S. S. “Induction motor fault diagnosis using ANFIS based on vibration signal spectrum analysis”, in Proc. 3rd International Conference on Frontiers of Signal Processing (ICFSP). Paris, France, 2017.

[3] Bouras, A. Bouras, S. and Kerfali, S. “Prediction of the mass unbalance of a variable speed induction motor by stator current multiple approaches”, Turkish Journal of Electrical Engineering & Computer Sciences., vol. 26, pp. 1056–1068, 2017.

[4] Gangsar, P., and Tiwari, R., “Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclasssupport vector machine algorithms”, Mechanical Systems and Signal Processing, vol. 94, pp. 464–481, 2017.

[5] Akçay, H., Germen, E., and Turkay, S. “Induction Motor Identification from Acoustic Noise Spectrum by a Covariance Subspace Algorithm”, in Proc. IEEE 14th International Conference on Control and Automation (ICCA). Anchorage, AK, USA, 2018.

[6] Glowacz, A. “Acoustic based fault diagnosis of three-phase induction motor”, Applied Acoustics, vol. 137, pp. 82–89, 2018.

[7] Hassan, O.E., Amer, M., Abdelsalam, A.K., and Williams, B.W. “Induction motor broken rotor bar fault detection techniques based on fault signature analysis – a review”, IET Electric Power Applications, vol. 12, no. 7, pp. 895–907, 2018.

[8] Chang, H., Kuo, C., Hsueh, Y., Wang, Y., and Hsieh, C. “Fuzzy-based fault diagnosis system for induction motors on smart grid structures”, in Proc. IEEE International Conference on Smart Energy Grid Engineering (SEGE). Oshawa, ON, Canada, 2017.

[9] Dong, M., Cheang, T., Booma, D.S., and Chan, S. “Fuzzy-expert diagnostics for detecting and locating internal faults in three phase induction motors”, Tsinghua Science and Technology, vol. 13, no. 6, pp. 817–822, 2008.

[10] Cho, H.C., Kim, K. S., Song, C.H., Lee, Y.J., and Lee. K.S. “Online fault detection and diagnosis algorithm based on probabilistic model for induction machines”, in Proc. 2008 SICE Annual Conference. Tokyo, Japan, 2008.

[11] Darwiche, A. “Modeling and reasoning with Bayesian networks”, Cambridge University Press, 2009.

Acta Universitatis Sapientiae, Electrical and Mechanical Engineering

The Journal of Sapientia Hungarian University of Transylvania

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 75 75 20
PDF Downloads 55 55 15